Foundry: Data Cloud Only

Get a Datafye Data Cloud running in under 5 minutes. Use your own algo containers to access normalized, low-latency market data.

What you'll have:

• Running Data Cloud with historical and live synthetic market data

• REST and WebSocket APIs for data access

• Your algo connected and consuming data

Prerequisites:

• Docker Desktop installed and running

• 8GB+ RAM available

• Python 3.7+ (optional - for testing with example algo)

Windows users: You need WSL to install the CLI. Click here for instructions

Setting up WSL for Windows

If you don't already have WSL installed, follow these steps:

1. Install WSL: Open PowerShell or Windows Command Prompt in administrator mode and run:

   wsl --install

   This will install Ubuntu by default.

2. Restart your computer when prompted

3. Launch WSL: Open the Start menu, search for "Ubuntu" (or your chosen Linux distribution), and click to launch

4. Complete initial setup: Create a username and password when prompted

Once WSL is installed and running, continue with the installation instructions below (WSL provides a Linux environment, so follow the Linux instructions).

For more details, see Microsoft's WSL installation guide.

Tip: Consider using Windows Terminal for a better terminal experience.

Step 1: Install the Datafye CLI

curl -fsSL https://downloads.n5corp.com/datafye/cli/latest/install.sh | sudo bash

Verify installation:

datafye

Expected output:

Datafye -- Institutional Grade Trading for Everyone

Datafye CLI is Datafye's command line tool. Try:

datafye help commands        list the top level commands
datafye help <command>       help on a specific command

No sudo access? See CLI Installation for alternative installation methods.

Step 2: Download Deployment Descriptor

curl -o quickstart.yaml https://downloads.n5corp.com/datafye/quickstarts/latest/foundry-data-cloud-only.yaml

This descriptor configures:

• Synthetic dataset with 10 symbols:

  • Tech: AAPL, MSFT, GOOGL, AMZN, NVDA

  • Growth: TSLA, META, NFLX, AMD, INTC

• Trades and quotes (tick data)

• 1-minute OHLC bars

• 90 days of historical data

• No API keys required

Step 3: Provision the Data Cloud

datafye foundry local provision --descriptor quickstart.yaml

Sneak Preview Note: This quickstart shows the expected workflow and interface. During the preview period, provisioning is not yet available and this command will display a "not yet implemented" message. This is expected behavior for the preview.

Expected Output (when available):

🚀 Provisioning Datafye Foundry (Data Cloud Only)...
✓ Validating descriptor
✓ Pulling Docker images
✓ Starting Data Cloud
✓ Loading synthetic data

✅ Data Cloud ready!

📊 Endpoints:
   REST:      http://localhost:8080/datafye-api/v1
   WebSocket: ws://localhost:8080/datafye-ws

Provisioning takes 3-5 minutes.

What Just Happened?

The deployment descriptor configured:

• Data Cloud - Normalized market data service with REST and WebSocket APIs

• Synthetic Dataset - 10 symbols with 90 days of historical data:

  • Tech: AAPL, MSFT, GOOGL, AMZN, NVDA

  • Growth: TSLA, META, NFLX, AMD, INTC

• Data Types - Trades, quotes, and 1-minute OHLC bars

What's running:

• Data Cloud container serving market data at http://localhost:8080

• Historical data loaded and ready to query

• Live synthetic data streaming in real-time

About the Synthetic Dataset: The Synthetic dataset attempts to generate realistic market data and is appropriate for demo and API testing purposes. It is NOT suitable for actual algo development - use real market data providers (Polygon, Alpaca, etc.) for developing and validating production algorithms.

Step 4: Test the Data Cloud

Option A: Python Test Script (Recommended)

Download and run the test script:

# Download test script (checks health, fetches historical bars, streams live trades)
curl -o test-data-cloud.py https://downloads.n5corp.com/datafye/quickstarts/test-data-cloud.py
python3 test-data-cloud.py

Output:

✓ Data Cloud is healthy
✓ Historical data available: 90 days of AAPL bars
✓ Latest AAPL: $195.34 @ 14:35:22
✓ Streaming AAPL trades...
  Trade: AAPL @ $195.35 (100 shares)
  Trade: AAPL @ $195.36 (250 shares)
  ...

Option B: CLI Commands

If Python isn't available:

# Check health
curl http://localhost:8080/datafye-api/v1/stocks/health/reference

# Get historical bars
curl "http://localhost:8080/datafye-api/v1/stocks/history/ohlcs?symbol=AAPL&interval=1m&limit=10"

# Stream live data
datafye foundry data stream --symbol AAPL

Step 5: Connect Your Algo

Download the example algo:

# Download simple momentum algo (compares current price to moving average, generates buy/sell signals)
curl -o my-algo.py https://downloads.n5corp.com/datafye/quickstarts/simple-momentum-algo.py
python3 my-algo.py

Output:

--- Momentum signals at 14:35:22 ---
🟢 AAPL: BUY ($195.34 > $194.82 avg)
🔴 MSFT: SELL ($412.15 < $413.21 avg)
🟢 GOOGL: BUY ($142.88 > $141.99 avg)
...

Your algo is now consuming data from the Datafye Data Cloud!

Manage Your Deployment

datafye foundry local stop        # Stop services
datafye foundry local start       # Restart services
datafye foundry local deprovision # Remove deployment

Next Steps

Build custom algos:

• Building Your First Algo - Own Container

• REST API Reference

• WebSocket API Reference

Customize your data:

• Data Descriptors Reference

• Data Access Modes

Upgrade to Full Stack:

• Foundry: Full Stack - Add backtesting and AI assistance

Troubleshooting

Docker not running: Start Docker Desktop and retry.

Port 8080 in use: Stop the conflicting service or see CLI Reference for custom ports.

No data returned: Wait 1-2 minutes after provisioning for data to load.